The Unscented Kalman Filter
نویسندگان
چکیده
In this book, the extended Kalman filter (EKF) has been used as the standard technique for performing recursive nonlinear estimation. The EKF algorithm, however, provides only an approximation to optimal nonlinear estimation. In this chapter, we point out the underlying assumptions and flaws in the EKF, and present an alternative filter with performance superior to that of the EKF. This algorithm, referred to as the unscented Kalman filter (UKF), was first proposed by Julier et al. [1–3], and further developed by Wan and van der Merwe [4–7]. The basic difference between the EKF and UKF stems from the manner in which Gaussian random variables (GRV) are represented for propagating through system dynamics. In the EKF, the state distribution is
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